A comparison of conditional autoregressive models used in Bayesian disease mapping
A comparison of conditional autoregressive models used in Bayesian disease mapping
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DOI：
10.1016/j.sste.2011.03.001
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发表时间：
2011-06-01
影响因子：
3.4
通讯作者：
Lee, Duncan
中科院分区：
文献类型：
--
作者：
Lee, Duncan
疾病分布图是流行病学的一个领域，它估计一个扩展的地理区域内疾病风险的空间模式，以便确定风险水平较高的地区。贝叶斯分层模型通常用于此背景下，其使用可用协变量数据和一组空间随机效应的组合来表示风险表面。纳入这些随机效应以模拟疾病数据中尚未通过可用协变量信息解释的任何过度分散或空间相关性。随机效应通常由条件自回归（CAR）先验分布建模，并且已经提出了许多替代规格。本文批评四个最常见的模型在CAR类，并通过模拟研究评估其适当性。这四个模型，然后应用到一个新的研究映射癌症发病率在大格拉斯哥，苏格兰，在2001年和2005年之间。(C)2011爱思唯尔有限公司版权所有。
Disease mapping is the area of epidemiology that estimates the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risk levels can be identified. Bayesian hierarchical models are typically used in this context, which represent the risk surface using a combination of available covariate data and a set of spatial random effects. These random effects are included to model any overdispersion or spatial correlation in the disease data, that has not been accounted for by the available covariate information. The random effects are typically modelled by a conditional autoregressive (CAR) prior distribution, and a number of alternative specifications have been proposed. This paper critiques four of the most common models within the CAR class, and assesses their appropriateness via a simulation study. The four models are then applied to a new study mapping cancer incidence in Greater Glasgow, Scotland, between 2001 and 2005. (C) 2011 Elsevier Ltd. All rights reserved.